Integration of Shuffled Frog Leaping Algorithm and Support Vector Regression for Prediction of Water Quality Parameters

Quality of surface water is a serious factor affecting human health and ecological systems. Accurate prediction of water quality parameters plays an important role in the management of rivers. Thus, different methods such as (support vector regression) SVR have been employed to predict water quality parameters. This paper applies SVR to predict eight water quality parameters including (sodium (Na+), potassium (K+), magnesium (Mg+2), sulfates (SO4-2), chloride (Cl-), power of hydrogen (pH), electrical conductivity (EC), and total dissolved solids (TDS)) at the Astane station in Sefidrood River, Iran. To achieve an efficient SVR model, the SVR parameters should be selected carefully. Commonly, various techniques such as trial and error, grid search and metaheuristic algorithms have been applied to estimate these parameters. This study presents a novel tool for estimation of quality parameters by coupling SVR and shuffled frog leaping algorithm (SFLA). Results of SFLA-SVR compared with genetic programming (GP) as a capable method in water quality prediction. Using SFLA-SVR, average of RMSE for training and testing of six combinations of data sets for all of the water quality parameters improved 57.4 % relative to GP. These results indicate that the new proposed SFLA-SVR tool is more efficient and powerful than GP for determining water quality parameters.